Snapshot range filters

ABSTRACT

In some examples, a method comprises: receiving a request to read data within a specified range from a backup file storing at least one base snapshot and at least one incremental snapshot; looking up the specified range in range filters from the backup file, the range filters corresponding to snapshots stored in the backup file and each range filter comprising bits indicating whether data exists at respective ranges within the snapshot corresponding to the respective range filter; and in response to the looking up, reading the requested data from the looked-up range in the backup file.

The present disclosure relates generally to computer architecture andcomputer architecture software for a data management platform and, insome more particular aspects, to methods and systems for searchingsnapshots.

BACKGROUND

The volume and complexity of data that is collected, analyzed and storedis increasing rapidly over time. The computer infrastructure used tohandle this data is also becoming more complex, with more processingpower and more portability. As a result, data management and storage isbecoming increasingly important. Significant issues of these processesinclude access to reliable data backup and storage, and fast datarecovery in cases of failure. Other aspects include data portabilityacross locations and platforms.

BRIEF SUMMARY

The present disclosure relates generally to searching snapshots for dataso that a file can be restored from backup.

In some examples, a method comprises: receiving a request to read datawithin a specified range from a backup file storing at least one basesnapshot and at least one incremental snapshot; looking up the specifiedrange in range filters from the backup file, the range filterscorresponding to snapshots stored in the backup file and each rangefilter comprising bits indicating whether data exists at respectiveranges within the snapshot corresponding to the respective range filter;and in response to the looking up, reading the requested data from thelooked-up range in the backup file.

A further example includes computer include computer apparatus having aprocessor; and a memory storing instructions that, when executed by theprocessor, configure the apparatus to execute the method. Anotherexample includes a non-transitory computer-readable storage medium; thecomputer-readable storage medium including instructions that whenexecuted by a computer, cause the computer to execute the method.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe views of the accompanying drawing:

FIG. 1 depicts one embodiment of a networked computing environment inwhich the disclosed technology may be practiced, according to an exampleembodiment.

FIG. 2 depicts one embodiment of the server of FIG. 1 , according to anexample embodiment.

FIG. 3 depicts one embodiment of the storage appliance of FIG. 1 ,according to an example embodiment.

FIG. 4 shows an example cluster of a distributed decentralized database,according to some example embodiments.

FIG. 5 depicts a range engine, according to some example embodiments,

FIG. 6 illustrates a range filter, according to some exampleembodiments.

FIG. 7 illustrates a method of generating a range filter bit vector,according to some example embodiments.

FIG. 8 illustrates a method of reading requested data from a backup fileusing the range filter bit vector, according to some exampleembodiments.

FIG. 9 illustrates a method of dynamic range filter sizing, according tosome example embodiments.

FIG. 10 illustrates range filter that is dynamically adjusted, accordingto some example embodiments.

FIG. 11 illustrates a backup file in patch format, according to someexample embodiments.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the present disclosure. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofexample embodiments. It will be evident, however, to one skilled in theart that the present inventive subject matter may be practiced withoutthese specific details.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright Rubrik, Inc., 018-2021, All Rights Reserved.

It will be appreciated that some of the examples disclosed herein aredescribed in the context of virtual machines that are backed up by usingbase and incremental snapshots, for example. This should not necessarilybe regarded as limiting of the disclosures. The disclosures, systems andmethods described herein apply not only to virtual machines of all typesthat run a file system (for example), but also to NAS devices, physicalmachines (for example Linux servers), and databases.

FIG. 1 depicts one embodiment of a networked computing environment 100in which the disclosed technology may be practiced. As depicted, thenetworked computing environment 100 includes a data center 106, astorage appliance 102, and a computing device 108 in communication witheach other via one or more networks 128. The networked computingenvironment 100 may also include a plurality of computing devicesinterconnected through one or more networks 128. The one or morenetworks 128 may allow computing devices and/or storage devices toconnect to and communicate with other computing devices and/or otherstorage devices. In some cases, the networked computing environment 100may include other computing devices and/or other storage devices notshown. The other computing devices may include, for example, a mobilecomputing device, a non-mobile computing device, a server, awork-station, a laptop computer, a tablet computer, a desktop computer,or an information processing system. The other storage devices mayinclude, for example, a storage area network storage device, anetworked-attached storage device, a hard disk drive, a solid-statedrive, or a data storage system.

The data center 106 may include one or more servers, such as server 200,in communication with one or more storage devices, such as storagedevice 104. The one or more servers may also be in communication withone or more storage appliances, such as storage appliance 102. Theserver 200, storage device 104, and storage appliance 300 may be incommunication with each other via a networking fabric connecting serversand data storage units within the data center 106 to each other. Thestorage appliance 300 may include a data management system for backingup virtual machines and/or files within a virtualized infrastructure.The server 200 may be used to create and manage one or more virtualmachines associated with a virtualized infrastructure.

The one or more virtual machines may run various applications, such as adatabase application or a web server. The storage device 104 may includeone or more hardware storage devices for storing data, such as a harddisk drive (HDD), a magnetic tape drive, a solid-state drive (SSD), astorage area network (SAN) storage device, or a Networked-AttachedStorage (NAS) device. In some cases, a data center, such as data center106, may include thousands of servers and/or data storage devices incommunication with each other. The one or more data storage devices 104may comprise a tiered data storage infrastructure (or a portion of atiered data storage infrastructure). The tiered data storageinfrastructure may allow for the movement of data across different tiersof a data storage infrastructure between higher-cost, higher-performancestorage devices (e.g., solid-state drives and hard disk drives) andrelatively lower-cost, lower-performance storage devices (e.g., magnetictape drives).

The one or more networks 128 may include a secure network such as anenterprise private network, an unsecure network such as a wireless opennetwork, a local area network (LAN), a wide area network (WAN), and theInternet. The one or more networks 128 may include a cellular network, amobile network, a wireless network, or a wired network. Each network ofthe one or more networks 128 may include hubs, bridges, routers,switches, and wired transmission media such as a direct-wiredconnection. The one or more networks 128 may include an extranet orother private network for securely sharing information or providingcontrolled access to applications or files.

A server, such as server 200, may allow a client to download informationor files (e.g., executable, text, application, audio, image, or videofiles) from the server 200 or to perform a search query related toparticular information stored on the server 200. In some cases, a servermay act as an application server or a file server. In general, server200 may refer to a hardware device that acts as the host in aclient-server relationship or a software process that shares a resourcewith or performs work for one or more clients.

One embodiment of server 200 includes a network interface 110, processor112, memory 114, disk 116, and virtualization manager 118 all incommunication with each other. Network interface 110 allows server 200to connect to one or more networks 128. Network interface 110 mayinclude a wireless network interface and/or a wired network interface.Processor 112 allows server 200 to execute computer-readableinstructions stored in memory 114 in order to perform processesdescribed herein. Processor 112 may include one or more processingunits, such as one or more CPUs and/or one or more GPUs. Memory 114 maycomprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM,EEPROM, Flash, etc.). Disk 116 may include a hard disk drive and/or asolid-state drive. Memory 114 and disk 116 may comprise hardware storagedevices.

The virtualization manager 118 may manage a virtualized infrastructureand perform management operations associated with the virtualizedinfrastructure. The virtualization manager 118 may manage theprovisioning of virtual machines running within the virtualizedinfrastructure and provide an interface to computing devices interactingwith the virtualized infrastructure. In one example, the virtualizationmanager 118 may set a virtual machine having a virtual disk into afrozen state in response to a snapshot request made via an applicationprogramming interface (API) by a storage appliance, such as storageappliance 300. Setting the virtual machine into a frozen state may allowa point in time snapshot of the virtual machine to be stored ortransferred. In one example, updates made to a virtual machine that hasbeen set into a frozen state may be written to a separate file (e.g., anupdate file) while the virtual disk may be set into a read-only state toprevent modifications to the virtual disk file while the virtual machineis in the frozen state.

The virtualization manager 118 may then transfer data associated withthe virtual machine (e.g., an image of the virtual machine or a portionof the image of the virtual disk file associated with the state of thevirtual disk at the point in time it is frozen) to a storage appliance(for example, a storage appliance 102 or storage appliance 300 of FIG. 1, described further below) in response to a request made by the storageappliance. After the data associated with the point in time snapshot ofthe virtual machine has been transferred to the storage appliance 300(for example), the virtual machine may be released from the frozen state(i.e., unfrozen) and the updates made to the virtual machine and storedin the separate file may be merged into the virtual disk file. Thevirtualization manager 118 may perform various virtual machine-relatedtasks, such as cloning virtual machines, creating new virtual machines,monitoring the state of virtual machines, moving virtual machinesbetween physical hosts for load balancing purposes, and facilitatingbackups of virtual machines.

One embodiment of a storage appliance 300 (or storage appliance 102)includes a network interface 120, processor 122, memory 124, and disk126 all in communication with each other. Network interface 120 allowsstorage appliance 300 to connect to one or more networks 128. Networkinterface 120 may include a wireless network interface and/or a wirednetwork interface. Processor 122 allows storage appliance 300 to executecomputer readable instructions stored in memory 124 in order to performprocesses described herein. Processor 122 may include one or moreprocessing units, such as one or more CPUs and/or one or more GPUs.Memory 124 may comprise one or more types of memory (e.g., RAM, SRAM,DRAM, ROM, EEPROM, NOR Flash, NAND Flash, etc.). Disk 126 may include ahard disk drive and/or a solid-state drive. Memory 124 and disk 126 maycomprise hardware storage devices.

In one embodiment, the storage appliance 300 may include four machines.Each of the four machines may include a multi-core CPU, 64 GB of RAM, a400 GB SSD, three 4 TB HDDs, and a network interface controller. In thiscase, the four machines may be in communication with the one or morenetworks 128 via the four network interface controllers. The fourmachines may comprise four nodes of a server cluster. The server clustermay comprise a set of physical machines that are connected together viaa network. The server cluster may be used for storing data associatedwith a plurality of virtual machines, such as backup data associatedwith different point-in-time versions of the virtual machines.

The networked computing environment 100 may provide a cloud computingenvironment for one or more computing devices. Cloud computing may referto Internet-based computing, wherein shared resources, software, and/orinformation may be provided to one or more computing devices on-demandvia the Internet. The networked computing environment 100 may comprise acloud computing environment providing Software-as-a-Service (SaaS) orInfrastructure-as-a-Service (IaaS) services. SaaS may refer to asoftware distribution model in which applications are hosted by aservice provider and made available to end users over the Internet. Inone embodiment, the networked computing environment 100 may include avirtualized infrastructure that provides software, data processing,and/or data storage services to end users accessing the services via thenetworked computing environment 100. In one example, networked computingenvironment 100 may provide cloud-based work productivity orbusiness-related applications to a computing device, such as computingdevice 108. The storage appliance 102 may comprise a cloud-based datamanagement system for backing up virtual machines and/or files within avirtualized infrastructure, such as virtual machines running on server200/or files stored on server 200.

In some cases, networked computing environment 100 may provide remoteaccess to secure applications and files stored within data center 106from a remote computing device, such as computing device 108. The datacenter 106 may use an access control application to manage remote accessto protected resources, such as protected applications, databases, orfiles located within the data center 106. To facilitate remote access tosecure applications and files, a secure network connection may beestablished using a virtual private network (VPN). A VPN connection mayallow a remote computing device, such as computing device 108, tosecurely access data from a private network (e.g., from a company fileserver or mail server) using an unsecure public network or the Internet.The VPN connection may require client-side software (e.g., running onthe remote computing device) to establish and maintain the VPNconnection. The VPN client software may provide data encryption andencapsulation prior to the transmission of secure private networktraffic through the Internet.

In some embodiments, the storage appliance 300 may manage the extractionand storage of virtual machine snapshots associated with different pointin time versions of one or more virtual machines running within the datacenter 106. A snapshot of a virtual machine may correspond with a stateof the virtual machine at a particular point-in-time. In response to arestore command from the storage device 104, the storage appliance 300may restore a point-in-time version of a virtual machine or restorepoint-in-time versions of one or more files located on the virtualmachine and transmit the restored data to the server 200. In response toa mount command from the server 200, the storage appliance 300 may allowa point-in-time version of a virtual machine to be mounted and allow theserver 200 to read and/or modify data associated with the point-in-timeversion of the virtual machine. To improve storage density, the storageappliance 300 may deduplicate and compress data associated withdifferent versions of a virtual machine and/or deduplicate and compressdata associated with different virtual machines. To improve systemperformance, the storage appliance 300 may first store virtual machinesnapshots received from a virtualized environment in a cache, such as aflash-based cache. The cache may also store popular data or frequentlyaccessed data (e.g., based on a history of virtual machine restorations,incremental files associated with commonly restored virtual machineversions) and current day incremental files or incremental filescorresponding with snapshots captured within the past 24 hours.

An incremental file may comprise a forward incremental file or a reverseincremental file. A forward incremental file may include a set of datarepresenting changes that have occurred since an earlier point-in-timesnapshot of a virtual machine. To generate a snapshot of the virtualmachine corresponding with a forward incremental file, the forwardincremental file may be combined with an earlier point in time snapshotof the virtual machine (e.g., the forward incremental file may becombined with the last full image of the virtual machine that wascaptured before the forward incremental file was captured and any otherforward incremental files that were captured subsequent to the last fullimage and prior to the forward incremental file). A reverse incrementalfile may include a set of data representing changes from a laterpoint-in-time snapshot of a virtual machine. To generate a snapshot ofthe virtual machine corresponding with a reverse incremental file, thereverse incremental file may be combined with a later point-in-timesnapshot of the virtual machine (e.g., the reverse incremental file maybe combined with the most recent snapshot of the virtual machine and anyother reverse incremental files that were captured prior to the mostrecent snapshot and subsequent to the reverse incremental file).

The storage appliance 300 may provide a user interface (e.g., aweb-based interface or a graphical user interface) that displays virtualmachine backup information such as identifications of the virtualmachines protected and the historical versions or time machine views foreach of the virtual machines protected. A time machine view of a virtualmachine may include snapshots of the virtual machine over a plurality ofpoints in time. Each snapshot may comprise the state of the virtualmachine at a particular point in time. Each snapshot may correspond witha different version of the virtual machine (e.g., Version 1 of a virtualmachine may correspond with the state of the virtual machine at a firstpoint in time and Version 2 of the virtual machine may correspond withthe state of the virtual machine at a second point in time subsequent tothe first point in time).

The user interface may enable an end user of the storage appliance 300(e.g., a system administrator or a virtualization administrator) toselect a particular version of a virtual machine to be restored ormounted. When a particular version of a virtual machine has beenmounted, the particular version may be accessed by a client (e.g., avirtual machine, a physical machine, or a computing device) as if theparticular version was local to the client. A mounted version of avirtual machine may correspond with a mount point directory (e.g.,/snapshots/VM5Nersion23). In one example, the storage appliance 300 mayrun an NFS server and make the particular version (or a copy of theparticular version) of the virtual machine accessible for reading and/orwriting. The end user of the storage appliance 300 may then select theparticular version to be mounted and run an application (e.g., a dataanalytics application) using the mounted version of the virtual machine.In another example, the particular version may be mounted as an iSCSItarget.

FIG. 2 depicts one embodiment of server 200 of FIG. 1 . The server 200may comprise one server out of a plurality of servers that are networkedtogether within a data center (e.g., data center 106). In one example,the plurality of servers may be positioned within one or more serverracks within the data center. As depicted, the server 200 includeshardware-level components and software-level components. Thehardware-level components include one or more processors 202, one ormore memory 204, and one or more disks 206. The software-levelcomponents include a hypervisor 208, a virtualized infrastructuremanager 222, and one or more virtual machines, such as virtual machine220. The hypervisor 208 may comprise a native hypervisor or a hostedhypervisor. The hypervisor 208 may provide a virtual operating platformfor running one or more virtual machines, such as virtual machine 220.Virtual machine 220 includes a plurality of virtual hardware devicesincluding a virtual processor 210, a virtual memory 212, and a virtualdisk 214. The virtual disk 214 may comprise a file stored within the oneor more disks 206. In one example, a virtual machine 220 may include aplurality of virtual disks 214, with each virtual disk of the pluralityof virtual disks 214 associated with a different file stored on the oneor more disks 206. Virtual machine 220 may include a guest operatingsystem 216 that runs one or more applications, such as application 218.

The virtualized infrastructure manager 222, which may correspond withthe virtualization manager 118 in FIG. 1 , may run on a virtual machineor natively on the server 200. The virtual machine may, for example, beor include the virtual machine 220 or a virtual machine separate fromthe server 200. Other arrangements are possible. The virtualizedinfrastructure manager 222 may provide a centralized platform formanaging a virtualized infrastructure that includes a plurality ofvirtual machines. The virtualized infrastructure manager 222 may managethe provisioning of virtual machines running within the virtualizedinfrastructure and provide an interface to computing devices interactingwith the virtualized infrastructure. The virtualized infrastructuremanager 222 may perform various virtualized infrastructure relatedtasks, such as cloning virtual machines, creating new virtual machines,monitoring the state of virtual machines, and facilitating backups ofvirtual machines.

In one embodiment, the server 200 may use the virtualized infrastructuremanager 222 to facilitate backups for a plurality of virtual machines(e.g., eight different virtual machines) running on the server 200. Eachvirtual machine running on the server 200 may run its own guestoperating system and its own set of applications. Each virtual machinerunning on the server 200 may store its own set of files using one ormore virtual disks associated with the virtual machine (e.g., eachvirtual machine may include two virtual disks that are used for storingdata associated with the virtual machine).

In one embodiment, a data management application running on a storageappliance, such as storage appliance 102 in FIG. 1 or storage appliance300 in FIG. 1 , may request a snapshot of a virtual machine running onserver 200. The snapshot of the virtual machine may be stored as one ormore files, with each file associated with a virtual disk of the virtualmachine. A snapshot of a virtual machine may correspond with a state ofthe virtual machine at a particular point in time. The particular pointin time may be associated with a time stamp. In one example, a firstsnapshot of a virtual machine may correspond with a first state of thevirtual machine (including the state of applications and files stored onthe virtual machine) at a first point in time and a second snapshot ofthe virtual machine may correspond with a second state of the virtualmachine at a second point in time subsequent to the first point in time.

In response to a request for a snapshot of a virtual machine at aparticular point in time, the virtualized infrastructure manager 222 mayset the virtual machine into a frozen state or store a copy of thevirtual machine at the particular point in time. The virtualizedinfrastructure manager 222 may then transfer data associated with thevirtual machine (e.g., an image of the virtual machine or a portion ofthe image of the virtual machine) to the storage appliance 300 orstorage appliance 102. The data associated with the virtual machine mayinclude a set of files including a virtual disk file storing contents ofa virtual disk of the virtual machine at the particular point in timeand a virtual machine configuration file storing configuration settingsfor the virtual machine at the particular point in time. The contents ofthe virtual disk file may include the operating system used by thevirtual machine, local applications stored on the virtual disk, and userfiles (e.g., images and word processing documents). In some cases, thevirtualized infrastructure manager 222 may transfer a full image of thevirtual machine to the storage appliance 102 or storage appliance 300 ofFIG. 1 or a plurality of data blocks corresponding with the full image(e.g., to enable a full image-level backup of the virtual machine to bestored on the storage appliance). In other cases, the virtualizedinfrastructure manager 222 may transfer a portion of an image of thevirtual machine associated with data that has changed since an earlierpoint in time prior to the particular point in time or since a lastsnapshot of the virtual machine was taken. In one example, thevirtualized infrastructure manager 222 may transfer only data associatedwith virtual blocks stored on a virtual disk of the virtual machine thathave changed since the last snapshot of the virtual machine was taken.In one embodiment, the data management application may specify a firstpoint in time and a second point in time and the virtualizedinfrastructure manager 222 may output one or more virtual data blocksassociated with the virtual machine that have been modified between thefirst point in time and the second point in time.

In some embodiments, the server 200 or the hypervisor 208 maycommunicate with a storage appliance, such as storage appliance 102 inFIG. 1 or storage appliance 300 in FIG. 1 , using a distributed filesystem protocol such as Network File System (NFS) Version 3, or ServerMessage Block (SMB) protocol. The distributed file system protocol mayallow the server 200 or the hypervisor 208 to access, read, write, ormodify files stored on the storage appliance as if the files werelocally stored on the server 200. The distributed file system protocolmay allow the server 200 or the hypervisor 208 to mount a directory or aportion of a file system located within the storage appliance.

FIG. 3 depicts one embodiment of storage appliance 300 in FIG. 1 . Thestorage appliance may include a plurality of physical machines that maybe grouped together and presented as a single computing system. Eachphysical machine of the plurality of physical machines may comprise anode in a cluster (e.g., a failover cluster). In one example, thestorage appliance may be positioned within a server rack within a datacenter. As depicted, the storage appliance 300 includes hardware-levelcomponents and software-level components. The hardware-level componentsinclude one or more physical machines, such as physical machine 314 andphysical machine 324. The physical machine 314 includes a networkinterface 316, processor 318, memory 320, and disk 322 all incommunication with each other. Processor 318 allows physical machine 314to execute computer readable instructions stored in memory 320 toperform processes described herein. Disk 322 may include a hard diskdrive and/or a solid-state drive. The physical machine 324 includes anetwork interface 326, processor 328, memory 330, and disk 332 all incommunication with each other.

Processor 328 allows physical machine 324 to execute computer readableinstructions stored in memory 330 to perform processes described herein.Disk 332 may include a hard disk drive and/or a solid-state drive. Insome cases, disk 332 may include a flash-based SSD or a hybrid HDD/SSDdrive. In one embodiment, the storage appliance 300 may include aplurality of physical machines arranged in a cluster (e.g., eightmachines in a cluster). Each of the plurality of physical machines mayinclude a plurality of multi-core CPUs, 108 GB of RAM, a 500 GB SSD,four 4 TB HDDs, and a network interface controller.

In some embodiments, the plurality of physical machines may be used toimplement a cluster-based network fileserver. The cluster-based networkfile server may neither require nor use a front-end load balancer. Oneissue with using a front-end load balancer to host the IP address forthe cluster-based network file server and to forward requests to thenodes of the cluster-based network file server is that the front-endload balancer comprises a single point of failure for the cluster-basednetwork file server. In some cases, the file system protocol used by aserver, such as server 200 in FIG. 1 , or a hypervisor, such ashypervisor 208 in FIG. 2 , to communicate with the storage appliance 300may not provide a failover mechanism (e.g., NFS Version 3). In the casethat no failover mechanism is provided on the client side, thehypervisor may not be able to connect to a new node within a cluster inthe event that the node connected to the hypervisor fails.

In some embodiments, each node in a cluster may be connected to eachother via a network and may be associated with one or more IP addresses(e.g., two different IP addresses may be assigned to each node). In oneexample, each node in the cluster may be assigned a permanent IP addressand a floating IP address and may be accessed using either the permanentIP address or the floating IP address. In this case, a hypervisor, suchas hypervisor 208 in FIG. 2 , may be configured with a first floating IPaddress associated with a first node in the cluster. The hypervisor mayconnect to the cluster using the first floating IP address. In oneexample, the hypervisor may communicate with the cluster using the NFSVersion 3 protocol. Each node in the cluster may run a Virtual RouterRedundancy Protocol (VRRP) daemon. A daemon may comprise a backgroundprocess. Each VRRP daemon may include a list of all floating IPaddresses available within the cluster. In the event that the first nodeassociated with the first floating IP address fails, one of the VRRPdaemons may automatically assume or pick up the first floating IPaddress if no other VRRP daemon has already assumed the first floatingIP address. Therefore, if the first node in the cluster fails orotherwise goes down, then one of the remaining VRRP daemons running onthe other nodes in the cluster may assume the first floating IP addressthat is used by the hypervisor for communicating with the cluster.

In order to determine which of the other nodes in the cluster willassume the first floating IP address, a VRRP priority may beestablished. In one example, given a number (N) of nodes in a clusterfrom node(0) to node(N−1), for a floating IP address (i), the VRRPpriority of nodeG) may be G-i) modulo N. In another example, given anumber (N) of nodes in a cluster from node(0) to node(N−1), for afloating IP address (i), the VRRP priority of nodeG) may be (i-j) moduloN. In these cases, nodeG) will assume floating IP address (i) only ifits VRRP priority is higher than that of any other node in the clusterthat is alive and announcing itself on the network. Thus, if a nodefails, then there may be a clear priority ordering for determining whichother node in the cluster will take over the failed node's floating IPaddress.

In some cases, a cluster may include a plurality of nodes and each nodeof the plurality of nodes may be assigned a different floating IPaddress. In this case, a first hypervisor may be configured with a firstfloating IP address associated with a first node in the cluster, asecond hypervisor may be configured with a second floating IP addressassociated with a second node in the cluster, and a third hypervisor maybe configured with a third floating IP address associated with a thirdnode in the cluster.

As depicted in FIG. 3 , the software-level components of the storageappliance 300 may include data management system 302, a virtualizationinterface 304, a distributed job scheduler 308, a distributed metadatastore 310, a distributed file system 312, and one or more virtualmachine search indexes, such as virtual machine search index 306. In oneembodiment, the software-level components of the storage appliance 300may be run using a dedicated hardware-based appliance. In anotherembodiment, the software-level components of the storage appliance 300may be run from the cloud (e.g., the software-level components may beinstalled on a cloud service provider).

In some cases, the data storage across a plurality of nodes in a cluster(e.g., the data storage available from the one or more physical machine(e.g., physical machine 314 and physical machine 324)) may be aggregatedand made available over a single file system namespace (e.g.,/snapshots/). A directory for each virtual machine protected using thestorage appliance 300 may be created (e.g., the directory for VirtualMachine A may be /snapshots/VM_A). Snapshots and other data associatedwith a virtual machine may reside within the directory for the virtualmachine. In one example, snapshots of a virtual machine may be stored insubdirectories of the directory (e.g., a first snapshot of VirtualMachine A may reside in /snapshots/VM_A/sl/ and a second snapshot ofVirtual Machine A may reside in /snapshots/VM_A/s2/).

The distributed file system 312 may present itself as a single filesystem, in which as new physical machines or nodes are added to thestorage appliance 300, the cluster may automatically discover theadditional nodes and automatically increase the available capacity ofthe file system for storing files and other data. Each file stored inthe distributed file system 312 may be partitioned into one or morechunks or shards. Each of the one or more chunks may be stored withinthe distributed file system 312 as a separate file. The files storedwithin the distributed file system 312 may be replicated or mirroredover a plurality of physical machines, thereby creating a load-balancedand fault tolerant distributed file system. In one example, storageappliance 300 may include ten physical machines arranged as a failovercluster and a first file corresponding with a snapshot of a virtualmachine (e.g., /snapshots/VM_A/sl/sl.full) may be replicated and storedon three of the ten machines.

The distributed metadata store 310 may include a distributed databasemanagement system that provides high availability without a single pointof failure. In one embodiment, the distributed metadata store 310 maycomprise a database, such as a distributed document-oriented database.The distributed metadata store 310 may be used as a distributed keyvalue storage system. In one example, the distributed metadata store 310may comprise a distributed NoSQL key value store database. In somecases, the distributed metadata store 310 may include a partitioned rowstore, in which rows are organized into tables or other collections ofrelated data held within a structured format within the key value storedatabase. A table (or a set of tables) may be used to store metadatainformation associated with one or more files stored within thedistributed file system 312. The metadata information may include thename of a file, a size of the file, file permissions associated with thefile, when the file was last modified, and file mapping informationassociated with an identification of the location of the file storedwithin a cluster of physical machines. In one embodiment, a new filecorresponding with a snapshot of a virtual machine may be stored withinthe distributed file system 312 and metadata associated with the newfile may be stored within the distributed metadata store 310. Thedistributed metadata store 310 may also be used to store a backupschedule for the virtual machine and a list of snapshots for the virtualmachine that are stored using the storage appliance 300.

In some cases, the distributed metadata store 310 may be used to manageone or more versions of a virtual machine. Each version of the virtualmachine may correspond with a full image snapshot of the virtual machinestored within the distributed file system 312 or an incremental snapshotof the virtual machine (e.g., a forward incremental or reverseincremental) stored within the distributed file system 312. In oneembodiment, the one or more versions of the virtual machine maycorrespond with a plurality of files. The plurality of files may includea single full image snapshot of the virtual machine and one or moreincremental aspects derived from the single full image snapshot. Thesingle full image snapshot of the virtual machine may be stored using afirst storage device of a first type (e.g., a HDD) and the one or moreincremental aspects derived from the single full image snapshot may bestored using a second storage device of a second type (e.g., an SSD). Inthis case, only a single full image needs to be stored and each versionof the virtual machine may be generated from the single full image orthe single full image combined with a subset of the one or moreincremental aspects. Furthermore, each version of the virtual machinemay be generated by performing a sequential read from the first storagedevice (e.g., reading a single file from a HDD) to acquire the fullimage and, in parallel, performing one or more reads from the secondstorage device (e.g., performing fast random reads from an SSD) toacquire the one or more incremental aspects.

The distributed job scheduler 308 may be used for scheduling backup jobsthat acquire and store virtual machine snapshots for one or more virtualmachines over time. The distributed job scheduler 308 may follow abackup schedule to back up an entire image of a virtual machine at aparticular point in time or one or more virtual disks associated withthe virtual machine at the particular point in time. In one example, thebackup schedule may specify that the virtual machine be backed up at asnapshot capture frequency, such as every two hours or every 24 hours.Each backup job may be associated with one or more tasks to be performedin a sequence. Each of the one or more tasks associated with a job maybe run on a particular node within a cluster. In some cases, thedistributed job scheduler 308 may schedule a specific job to be run on aparticular node based on data stored on the particular node. Forexample, the distributed job scheduler 308 may schedule a virtualmachine snapshot job to be run on a node in a cluster that is used tostore snapshots of the virtual machine in order to reduce networkcongestion.

The distributed job scheduler 308 may comprise a distributed faulttolerant job scheduler, in which jobs affected by node failures arerecovered and rescheduled to be run on available nodes. In oneembodiment, the distributed job scheduler 308 may be fully decentralizedand implemented without the existence of a master node. The distributedjob scheduler 308 may run job scheduling processes on each node in acluster or on a plurality of nodes in the cluster. In one example, thedistributed job scheduler 308 may run a first set of job schedulingprocesses on a first node in the cluster, a second set of job schedulingprocesses on a second node in the cluster, and a third set of jobscheduling processes on a third node in the cluster. The first set ofjob scheduling processes, the second set of job scheduling processes,and the third set of job scheduling processes may store informationregarding jobs, schedules, and the states of jobs using a metadatastore, such as distributed metadata store 310. In the event that thefirst node running the first set of job scheduling processes fails(e.g., due to a network failure or a physical machine failure), thestates of the jobs managed by the first set of job scheduling processesmay fail to be updated within a threshold period of time (e.g., a jobmay fail to be completed within 30 seconds or within minutes from beingstarted). In response to detecting jobs that have failed to be updatedwithin the threshold period of time, the distributed job scheduler 308may undo and restart the failed jobs on available nodes within thecluster.

The job scheduling processes running on at least a plurality of nodes ina cluster (e.g., on each available node in the cluster) may manage thescheduling and execution of a plurality of jobs. The job schedulingprocesses may include run processes for running jobs, cleanup processesfor cleaning up failed tasks, and rollback processes for rolling-back orundoing any actions or tasks performed by failed jobs. In oneembodiment, the job scheduling processes may detect that a particulartask for a particular job has failed and in response may perform acleanup process to clean up or remove the effects of the particular taskand then perform a rollback process that processes one or more completedtasks for the particular job in reverse order to undo the effects of theone or more completed tasks. Once the particular job with the failedtask has been undone, the job scheduling processes may restart theparticular job on an available node in the cluster.

The distributed job scheduler 308 may manage a job in which a series oftasks associated with the job are to be performed atomically (i.e.,partial execution of the series of tasks is not permitted). If theseries of tasks cannot be completely executed or there is any failurethat occurs to one of the series of tasks during execution (e.g., a harddisk associated with a physical machine fails or a network connection tothe physical machine fails), then the state of a data management systemmay be returned to a state as if none of the series of tasks was everperformed. The series of tasks may correspond with an ordering of tasksfor the series of tasks and the distributed job scheduler 308 may ensurethat each task of the series of tasks is executed based on the orderingof tasks. Tasks that do not have dependencies with each other may beexecuted in parallel.

In some cases, the distributed job scheduler 308 may schedule each taskof a series of tasks to be performed on a specific node in a cluster. Inother cases, the distributed job scheduler 308 may schedule a first taskof the series of tasks to be performed on a first node in a cluster anda second task of the series of tasks to be performed on a second node inthe cluster. In these cases, the first task may have to operate on afirst set of data (e.g., a first file stored in a file system) stored onthe first node and the second task may have to operate on a second setof data (e.g., metadata related to the first file that is stored in adatabase) stored on the second node. In some embodiments, one or moretasks associated with a job may have an affinity to a specific node in acluster.

In one example, if the one or more tasks require access to a databasethat has been replicated on three nodes in a cluster, then the one ormore tasks may be executed on one of the three nodes. In anotherexample, if the one or more tasks require access to multiple chunks ofdata associated with a virtual disk that has been replicated over fournodes in a cluster, then the one or more tasks may be executed on one ofthe four nodes. Thus, the distributed job scheduler 308 may assign oneor more tasks associated with a job to be executed on a particular nodein a cluster based on the location of data required to be accessed bythe one or more tasks.

In one embodiment, the distributed job scheduler 308 may manage a firstjob associated with capturing and storing a snapshot of a virtualmachine periodically (e.g., every 30 minutes). The first job may includeone or more tasks, such as communicating with a virtualizedinfrastructure manager, such as the virtualized infrastructure manager222 in FIG. 2 , to create a frozen copy of the virtual machine and totransfer one or more chunks (or one or more files) associated with thefrozen copy to a storage appliance, such as storage appliance 300 inFIG. 1 . The one or more tasks may also include generating metadata forthe one or more chunks, storing the metadata using the distributedmetadata store 310, storing the one or more chunks within thedistributed file system 312, and communicating with the virtualizedinfrastructure manager 222 that the frozen copy of the virtual machinemay be unfrozen or released from a frozen state. The metadata for afirst chunk of the one or more chunks may include information specifyinga version of the virtual machine associated with the frozen copy, a timeassociated with the version (e.g., the snapshot of the virtual machinewas taken at 5:30 p.m. on Jun. 29, 2018), and a file path to where thefirst chunk is stored within the distributed file system 92 (e.g., thefirst chunk is located at /snapshotsNM_B/sl/sl.chunkl). The one or moretasks may also include deduplication, compression (e.g., using alossless data compression algorithm such as LZ4 or LZ77), decompression,encryption (e.g., using a symmetric key algorithm such as Triple DES orAES-256), and decryption related tasks.

The virtualization interface 304 may provide an interface forcommunicating with a virtualized infrastructure manager managing avirtualization infrastructure, such as virtualized infrastructuremanager 222 in FIG. 2 , and requesting data associated with virtualmachine snapshots from the virtualization infrastructure. Thevirtualization interface 304 may communicate with the virtualizedinfrastructure manager using an Application Programming Interface (API)for accessing the virtualized infrastructure manager (e.g., tocommunicate a request for a snapshot of a virtual machine). In thiscase, storage appliance 300 may request and receive data from avirtualized infrastructure without requiring agent software to beinstalled or running on virtual machines within the virtualizedinfrastructure. The virtualization interface 304 may request dataassociated with virtual blocks stored on a virtual disk of the virtualmachine that have changed since a last snapshot of the virtual machinewas taken or since a specified prior point in time. Therefore, in somecases, if a snapshot of a virtual machine is the first snapshot taken ofthe virtual machine, then a full image of the virtual machine may betransferred to the storage appliance. However, if the snapshot of thevirtual machine is not the first snapshot taken of the virtual machine,then only the data blocks of the virtual machine that have changed sincea prior snapshot was taken may be transferred to the storage appliance.

The virtual machine search index 306 may include a list of files thathave been stored using a virtual machine and a version history for eachof the files in the list. Each version of a file may be mapped to theearliest point-in-time snapshot of the virtual machine that includes theversion of the file or to a snapshot of the virtual machine thatincludes the version of the file (e.g., the latest point in timesnapshot of the virtual machine that includes the version of the file).In one example, the virtual machine search index 306 may be used toidentify a version of the virtual machine that includes a particularversion of a file (e.g., a particular version of a database, aspreadsheet, or a word processing document). In some cases, each of thevirtual machines that are backed up or protected using storage appliance300 may have a corresponding virtual machine search index.

In one embodiment, as each snapshot of a virtual machine is ingested,each virtual disk associated with the virtual machine is parsed in orderto identify a file system type associated with the virtual disk and toextract metadata (e.g., file system metadata) for each file stored onthe virtual disk. The metadata may include information for locating andretrieving each file from the virtual disk. The metadata may alsoinclude a name of a file, the size of the file, the last time at whichthe file was modified, and a content checksum for the file. Each filethat has been added, deleted, or modified since a previous snapshot wascaptured may be determined using the metadata (e.g., by comparing thetime at which a file was last modified with a time associated with theprevious snapshot). Thus, for every file that has existed within any ofthe snapshots of the virtual machine, a virtual machine search index maybe used to identify when the file was first created (e.g., correspondingwith a first version of the file) and at what times the file wasmodified (e.g., corresponding with subsequent versions of the file).Each version of the file may be mapped to a particular version of thevirtual machine that stores that version of the file.

In some cases, if a virtual machine includes a plurality of virtualdisks, then a virtual machine search index may be generated for eachvirtual disk of the plurality of virtual disks. For example, a firstvirtual machine search index may catalog and map files located on afirst virtual disk of the plurality of virtual disks and a secondvirtual machine search index may catalog and map files located on asecond virtual disk of the plurality of virtual disks. In this case, aglobal file catalog or a global virtual machine search index for thevirtual machine may include the first virtual machine search index andthe second virtual machine search index. A global file catalog may bestored for each virtual machine backed up by a storage appliance withina file system, such as distributed file system 312 in FIG. 3 .

The data management system 302 may comprise an application running onthe storage appliance 300 that manages and stores one or more snapshotsof a virtual machine. In one example, the data management system 302 maycomprise a highest-level layer in an integrated software stack runningon the storage appliance. The integrated software stack may include thedata management system 302, the virtualization interface 304, thedistributed job scheduler 308, the distributed metadata store 310, andthe distributed file system 312.

In some cases, the integrated software stack may run on other computingdevices, such as a server or computing device 108 in FIG. 1 . The datamanagement system 302 may use the virtualization interface 304, thedistributed job scheduler 308, the distributed metadata store 310, andthe distributed file system 312 to manage and store one or moresnapshots of a virtual machine. Each snapshot of the virtual machine maycorrespond with a point-in-time version of the virtual machine. The datamanagement system 302 may generate and manage a list of versions for thevirtual machine. Each version of the virtual machine may map to orreference one or more chunks and/or one or more files stored within thedistributed file system 312. Combined together, the one or more chunksand/or the one or more files stored within the distributed file system312 may comprise a full image of the version of the virtual machine.

FIG. 4 shows an example cluster 400 of a distributed decentralizeddatabase, according to some example embodiments. As illustrated, theexample cluster 400 includes five nodes, nodes 1-5. In some exampleembodiments, each of the five nodes runs from different machines, suchas physical machine 314 in FIG. 3 or virtual machine 220 in FIG. 2 . Thenodes in the example cluster 400 can include instances of peer nodes ofa distributed database (e.g., cluster-based database, distributeddecentralized database management system, a NoSQL database, ApacheCassandra, DataStax, MongoDB, CouchDB), according to some exampleembodiments. The distributed database system is distributed in that datais sharded or distributed across the example cluster 400 in shards orchunks and decentralized in that there is no central storage device andno single point of failure. The system operates under an assumption thatmultiple nodes may go down, up, or become non-responsive, and so on.Sharding is splitting up of the data horizontally and managing eachshard separately on different nodes. For example, if the data managed bythe example cluster 400 can be indexed using the 26 letters of thealphabet, node 1 can manage a first shard that handles records thatstart with A through E, node 2 can manage a second shard that handlesrecords that start with F through J, and so on.

In some example embodiments, data written to one of the nodes isreplicated to one or more other nodes per a replication protocol of theexample cluster 400. For example, data written to node 1 can bereplicated to nodes 2 and 3. If node 1 prematurely terminates, node 2and/or 3 can be used to provide the replicated data. In some exampleembodiments, each node of example cluster 400 frequently exchanges stateinformation about itself and other nodes across the example cluster 400using gossip protocol. Gossip protocol is a peer-to-peer communicationprotocol in which each node randomly shares (e.g., communicates,requests, transmits) location and state information about the othernodes in a given cluster.

Writing: For a given node, a sequentially written commit log capturesthe write activity to ensure data durability. The data is then writtento an in-memory structure (e.g., a memtable, write-back cache). Eachtime the in-memory structure is full, the data is written to disk in aSorted String Table data file. In some example embodiments, writes areautomatically partitioned and replicated throughout the example cluster400.

Reading: Any node of example cluster 400 can receive a read request(e.g., query) from an external client. If the node that receives theread request manages the data requested, the node provides the requesteddata. If the node does not manage the data, the node determines whichnode manages the requested data. The node that received the read requestthen acts as a proxy between the requesting entity and the node thatmanages the data (e.g., the node that manages the data sends the data tothe proxy node, which then provides the data to an external entity thatgenerated the request).

The distributed decentralized database system is decentralized in thatthere is no single point of failure due to the nodes being symmetricaland seamlessly replaceable. For example, whereas conventionaldistributed data implementations have nodes with different functions(e.g., master/slave nodes, asymmetrical database nodes, federateddatabases), the nodes of example cluster 400 are configured to functionthe same way (e.g., as symmetrical peer database nodes that communicatevia gossip protocol, such as Cassandra nodes) with no single point offailure. If one of the nodes in example cluster 400 terminatesprematurely (“goes down”), another node can rapidly take the place ofthe terminated node without disrupting service. The example cluster 400can be a container for a keyspace, which is a container for data in thedistributed decentralized database system (e.g., whereas a database is acontainer for containers in conventional relational databases, theCassandra keyspace is a container for a Cassandra database system).

FIG. 5 depicts a range engine 502, according to some exampleembodiments. The range engine 502 generates a range filter 510 of abackup file, such as a patch file. A first snapshot (base) usuallycontains the whole data being backed up, but subsequent snapshotsusually only contain the changes since the previous snapshot (in orderto save backup time and storage space).

As the snapshot chain grows longer, read performance gets worse becausedata has to be looked up in each snapshot. In order to reduce thesnapshot chain length, the snapshot chain is Reversed/Consolidated (ifsnapshots expire). One of the main reasons to reverse a snapshot chainis to improve read performance.

The range engine 502 allows for very quick checks if a particularincremental snapshot contains data for a range or not. Incrementalsnapshots are usually sparse and do not contain data for a lot ofranges. Improving read performance in long snapshot chains will reducethe need for reverses. There are also other advantages of doing this:

1. Improve read performance of archival snapshot chains significantly.2. Improve read performance of long journal chains; 3. Improve readperformance of long patch file chains. Using range filters in patchfiles/journals improved read performance for large chains by up to 20%.Performance of dry reads (where only index blocks are read and notactually data blocks for a range) improved by up to 3×.

Backup files may be stored in patch file format, which can be atwo-level index key value store. The file comprises data blocks followedby index blocks. Each index block contains information about the datablocks which come just before it. The final root index block containsinformation about all the index blocks. The root index block is storedin memory and the other index blocks are fetched on demand from disk.The range filter 510 can be stored in the root index block. A journalfile is a log file which has a sequence of serialized extents. A journalhas a corresponding index file to index into the journal file for aparticular range lookup. A journal's index file can also be a two-levelindex key value store.

In one example, as seen in FIG. 11 , each data block has 128 k blobs,each blob holds 128 KB of logical user data and each data section holds16 GB of logical user data. Each index block is 128K index records witha size of 2 MB. The root index block, which can hold the range filter510, can be 2310 bytes for a 4 TB patch file.

The range filter 510 which may be stored as a bit vector where each bitrepresents whether the backup file (e.g., patch file or journal) hasdata for that range. As the range filter can be stored in the root indexblock of the two-level key value (kv) store, it will be loaded intomemory upon opening the two-level kv store. With the range filter 510,by checking an in-memory bit it can be determined if a snapshot in thebackup file has data for that range. Range filters can give falsepositives so the range size needs to be chosen appropriately for the usecase. Large range size would mean smaller range filter sizes (fewer bitsare required), but less accurate range filters. The size of the bitvector required is LOGICAL_SIZE/(RANGE_SIZE*8) bytes. So to cover a 4 TBlogical size with a 64 MB range size, we would need an 8 KB of space inthe root index block of the two-level kv store.

The range engine 502, comprises an indexer 504, a searcher 506, adynamic indexer 508, and a range filter 510 (which may be stored in thebackup file or separately). During operation of the range engine 502 theindexer 504 scans each snapshot stored in a backup file, generates arange filter 510 for each snapshot and stores the generated rangefilters 510 in the backup file (e.g., in the root index) or elsewhere.The indexer 504 may execute method 700, which will be discussed furtherin conjunction with FIG. 7 . The searcher 506, when receiving a requestto search for data in backup, e.g., to recover a file, searches thegenerated range filters 510 to see if data is in range in the snapshotand only then reads the relevant index block to pull the data. Thesearcher 506 can execute the method 800, which will be discussed infurther detail in conjunction with FIG. 8 .

The dynamic indexer 508 adjusts a range size of the generated rangefilters 510 by ORing adjacent bits as a backup file grows in size aswill be discussed in further detail in conjunction with FIG. 9 . Thiscan be set manually or done automatically whenever the backup file growsa predefined amount to ensure speedy searching. Note that the dynamicindexer 508 can adjust a subset of the generated range filters 510 andretain a lower range size for the remaining range filters 510.

FIG. 6 illustrates a range filter 510, according to some exampleembodiments. In an embodiment, there is one range filter for eachsnapshot stored in the backup file. Range filter size may be constantfor all range filters or may vary for each range filter or a subset ofrange filters. In the example of FIG. 6 , the range size is set to 20and the corresponding incremental snapshot has data at offsets 50-85,150-79 and 220-225. Accordingly, the example range filter of FIG. 6 willhave bits set to 1 at ranges 40-60, 60-80, 140-160, 160-180, and220-240. Each range filter 510 may also include a field or fields (notshown) identifying its corresponding snapshot and/or range size.

With the range filter 510, by just checking an in-memory bit a requestorcan find out if a backup file (e.g., incremental snapshot or a journal)has data for that range. If the bit in the range filter corresponding toa range is 0, then that range is surely not contained in the backupfile. If the bit is 1, then the backup file may contain the data for therequested range. Range filters can give false positives so the rangesize needs to be chosen appropriately for the use case. Large range sizewould mean smaller range filter sizes (fewer bits are required), butless accurate range filters. The size of the bit vector required isLOGICAL_SIZE/(RANGE_SIZE*8) bytes. So to cover a 4 TB logical size witha 64 MB range size, we would need an 8 KB of space in the root indexblock of the backup file.

FIG. 7 illustrates a method 700 of generating a range filter 510,according to some example embodiments. Initially, a range filter rangesize can be set in block 702. This may be a default range size oradjusted based on the size of backup file or snapshots therein. As willbe discussed further in conjunction with FIG. 9 , the range filter sizecan also be adjusted dynamically. Next, at block 704, snapshots withinthe backup file are scanned at the set range size set at block 702.Scanning to see if data is present can be done scanning a correspondingindex block for the snapshot in an embodiment. Based on the results ofthe scan, a range filter 510 is generated at block 706 for each snapshotsuch as in the example of FIG. 6 . The generated range filters rangefilter 510 are then stored at block 708 in a root index of the backupfile and/or elsewhere. Range filters may also be generated whilecreating an incremental snapshot file or a two level index kv store,thus avoiding extra I/O required for it's generation.

FIG. 8 illustrates a method 800 of reading requested data from a backupfile using the range filter (which can be stored as a bit vector),according to some example embodiments. At block 802 the range filters510 are read into memory. This can include the range size for the rangefilters 510 if not constant or predefined (e.g., using a defaultsetting). At block 804 a request to read data within a specified rangefrom a backup file storing at least one base snapshot and at least oneincremental snapshot is received. This may be for the purpose of, forexample, restoring an individual file or consolidating snapshots.

At block 806, the specified range is looked up in a range filters (bitvector) from the backup file corresponding to snapshots stored in thebackup file. As mentioned previously, each range filter comprises bitsindicating whether data exists at respective ranges within the snapshotcorresponding to the range filter. If a range filter indicates that abackup file does not contain the range (the bit for the range is 0),then lookup in that backup file is skipped, this avoiding the disk 1/Oof loading the index block of that backup file. After the range isidentified via the looking up at block 806, the requested data is readfrom the looked-up range in the backup file from relevant snapshotsstored therein at block 808. The read data can then be passed to therequesting entity (e.g, search entity to return search results, filerestore entity for use in a file restore operation, etc.).

FIG. 9 illustrates a method 900 of dynamic range filter sizing,according to some example embodiments. Range filter sizing can be set bya user or automatically adjusted as a backup file increases in size(e.g., double a range filter size when a backup file doubles in range orsize). In method 900, first, the dynamic indexer 508 reads at block 902the bit range filter of each snapshot in the backup file. Next,contiguous bits are merged with an OR operation at block 904. Finally,the read range filters are replaced with the merged range filters in theroot index and/or elsewhere at block 906.

An example of a dynamically adjusted range filter can be seen in FIG. 10. The range filter size is being increased from 20 to 40. Accordingly,in the adjusting range filter, a bit is set to 0 at offset 0-40 as 0 OR0 in original offsets (0-20, 20-40) is 0. However, at offset 40-80 inthe adjusted range filter is set to 1 as 1 OR 1 from the original rangefilter (at offsets 40-60 and 60-80) is 1.

For example, the range filter can initially have a maximum size of 8 KBand set a fine grained range size, such as 1 MB, which can thereforecover a logical size of 64 GB. If a write comes for an offset greaterthan the supported logical size, the range size of the range filter canbe doubled while keeping the range filter size the same. Keeping therange filter size the same has the advantage of not using any extramemory while supporting the required logical size. This makes the rangefilter less accurate, but can increase the max range size supported.Using dynamic sizing, the range filter can be fine grained range, andthen grow dynamically based on the logical size needed to support asmore writes come into the backup file.

In view of the disclosure above, various examples are set forth below.It should be noted that one or more features of an example, taken inisolation or combination, should be considered within the disclosure ofthis application.

1. A method, comprising:

receiving a request to read data within a specified range from a backupfile storing at least one base snapshot and at least one incrementalsnapshot;

looking up the specified range in range filters from the backup file,the range filters corresponding to snapshots stored in the backup fileand each range filter comprising bits indicating whether data exists atrespective ranges within the snapshot corresponding to the respectiverange filter; and

in response to the looking up, reading the requested data from thelooked-up range in the backup file.

2. The method of example 1, wherein each range filter is generated by:

setting a range filter size;

scanning snapshots for data at the set range size;

generating corresponding range filters for each snapshot based on thescanning; and

storing the generated range filters in a root index of the backup file.

3. The method of any of the prior examples, further comprising adjustingthe range filter size as the backup file increases in size.

4. The method of any of the prior examples, wherein the adjusting therange filter size doubles the range filter size when the backup filedoubles in size.

5. The method of any of the prior examples, wherein the backup file istwo-level index key value store.

6. The method of any of the prior examples, wherein each range filter isgenerated by:

setting a range filter size;

scanning snapshot indices for data at the set range size;

generating corresponding range filters for each snapshot index based onthe scanning; and

storing the generated range filters in a root index of the backup file.

7. The method of any of the prior examples, further comprising restoringa backed-up file using the read requested data.

8. The method of any of the prior examples, wherein the range filtersare stored as range filter bit vectors.

9. The method of any of the prior examples, further comprising reading aroot index having the range filters into memory without reading snapshotindices into memory.

10. A non-transitory computer-readable storage medium, thecomputer-readable storage medium including instructions that whenexecuted by a computer, cause the computer to:

receive a request to read data within a specified range from a backupfile storing at least one base snapshot and at least one incrementalsnapshot;

look up the specified range in range filters from the backup file, therange filters corresponding to snapshots stored in the backup file andeach range filter comprising bits indicating whether data exists atrespective ranges within the snapshot corresponding to the respectiverange filter; and

in response to the looking up, reading the requested data from thelooked-up range in the backup file.

11. A computing apparatus comprising:

a processor; and

a memory storing instructions that, when executed by the processor,configure the apparatus to:

-   -   receive a request to read data within a specified range from a        backup file storing at least one base snapshot and at least one        incremental snapshot;    -   look up the specified range in range filters from the backup        file, the range filters corresponding to snapshots stored in the        backup file and each range filter comprising bits indicating        whether data exists at respective ranges within the snapshot        corresponding to the respective range filter; and    -   in response to the looking up, reading the requested data from        the looked-up range in the backup file.

12. The computing apparatus of claim 11, wherein each range filter isgenerated by:

setting a range filter size;

scanning snapshots for data at the set range size;

generating corresponding range filters for each snapshot based on thescanning; and

storing the generated range filters in a root index of the backup file.

13. The computing apparatus of any of the prior examples, wherein theinstructions further configure the apparatus to adjust the range filtersize as the backup file increases in size.

14. The computing apparatus of any of the prior examples, wherein theadjusting the range filter size doubles the range filter size when thebackup file doubles in size.

15. The computing apparatus of any of the prior examples, wherein therange filter is a bit vector and the adjusting the range filter sizedoubles the range filter size by ORing adjacent bits in the rangefilter.

16. The computing apparatus of any of the prior examples, wherein thebackup file is two-level index key value store.

17. The computing apparatus of any of the prior examples, wherein eachrange filter is generated by:

setting a range filter size;

scanning snapshot indices for data at the set range size;

generating corresponding range filters for each snapshot index based onthe scanning; and

storing the generated range filters in a root index of the backup file.

18. The computing apparatus of any of the prior examples, wherein theinstructions further configure the apparatus to restore a backed-up fileusing the read requested data.

19. The computing apparatus of any of the prior examples, wherein therange filters are stored as range filter bit vectors.

20. The computing apparatus of any of the prior examples, wherein theinstructions configure the apparatus to read a root index having therange filters into memory without reading snapshot indices into memory.

The terms “machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

Although examples have been described with reference to specific exampleembodiments or methods, it will be evident that various modificationsand changes may be made to these embodiments without departing from thebroader scope of the embodiments. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense. The accompanying drawings that form a part hereof, show by way ofillustration, and not of limitation, specific embodiments in which thesubject matter may be practiced. The embodiments illustrated aredescribed in sufficient detail to enable those skilled in the art topractice the teachings disclosed herein. Other embodiments may beutilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This detailed description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

1. A method, comprising: receiving a request to read data within aspecified logical address range from a backup file storing at least onebase snapshot and at least one incremental snapshot; looking up thespecified logical address range in range filters associated with thebackup file, the range filters corresponding to respective snapshotsstored in the backup file and each range filter comprising bitsindicating whether a corresponding snapshot includes data within acorresponding logical address range of the backup file that is spannedby the range filter, wherein at least two of the range filters span atleast the specified logical address range from the backup file; and inresponse to the looking up, reading the requested data from thelooked-up range in the backup file.
 2. The method of claim 1, whereineach range filter is generated by: setting a range filter size; scanningsnapshots for data at the set range size; generating corresponding rangefilters for each snapshot based on the scanning; and storing thegenerated range filters in a root index of the backup file.
 3. Themethod of claim 2, further comprising adjusting the range filter size asthe backup file increases in size.
 4. The method of claim 3, wherein theadjusting the range filter size doubles the range filter size when thebackup file doubles in size.
 5. The method of claim 1, wherein thebackup file is a two-level index key value store.
 6. The method of claim1, wherein each range filter is generated by: setting a range filtersize; scanning snapshot indices for data at the set range size;generating corresponding range filters for each snapshot index based onthe scanning; and storing the generated range filters in a root index ofthe backup file.
 7. The method of claim 1, further comprising restoringa backed-up file using the read requested data.
 8. The method of claim1, wherein the range filters are stored as range filter bit vectors. 9.The method of claim 1, further comprising reading a root index havingthe range filters into memory without reading snapshot indices intomemory.
 10. A non-transitory computer-readable storage medium, thecomputer-readable storage medium including instructions that whenexecuted by a computer, cause the computer to: receive a request to readdata within a specified logical address range from a backup file storingat least one base snapshot and at least one incremental snapshot; lookup the specified logical address range in range filters associated withthe backup file, the range filters corresponding to respective snapshotsstored in the backup file and each range filter comprising bitsindicating whether a corresponding snapshot includes data within acorresponding logical address range of the backup file that is spannedby the range filter, wherein at least two of the range filters span atleast the specified logical address range from the backup file; and inresponse to the looking up, reading the requested data from thelooked-up range in the backup file.
 11. A computing apparatuscomprising: a processor; and a memory storing instructions that, whenexecuted by the processor, configure the apparatus to: receive a requestto read data within a specified logical address range from a backup filestoring at least one base snapshot and at least one incrementalsnapshot; look up the specified logical address range in range filtersassociated with the backup file, the range filters corresponding torespective snapshots stored in the backup file and each range filtercomprising bits indicating whether a corresponding snapshot includesdata within a corresponding logical address range of the backup filethat is spanned by the range filter, wherein at least two of the rangefilters span at least the specified logical address range from thebackup file; and in response to the looking up, reading the requesteddata from the looked-up range in the backup file.
 12. The computingapparatus of claim 11, wherein each range filter is generated by:setting a range filter size; scanning snapshots for data at the setrange size; generating corresponding range filters for each snapshotbased on the scanning; and storing the generated range filters in a rootindex of the backup file.
 13. The computing apparatus of claim 12,wherein the instructions further configure the apparatus to adjust therange filter size as the backup file increases in size.
 14. Thecomputing apparatus of claim 13, wherein the adjusting the range filtersize doubles the range filter size when the backup file doubles in size.15. The computing apparatus of claim 13, wherein the range filter is abit vector and the adjusting the range filter size doubles the rangefilter size by ORing adjacent bits in the range filter.
 16. Thecomputing apparatus of claim 11, wherein the backup file is a two-levelindex key value store.
 17. The computing apparatus of claim 11, whereineach range filter is generated by: setting a range filter size; scanningsnapshot indices for data at the set range size; generatingcorresponding range filters for each snapshot index based on thescanning; and storing the generated range filters in a root index of thebackup file.
 18. The computing apparatus of claim 11, wherein theinstructions further configure the apparatus to restore a backed-up fileusing the read requested data.
 19. The computing apparatus of claim 11,wherein the range filters are stored as range filter bit vectors. 20.The computing apparatus of claim 11, wherein the instructions configurethe apparatus to read a root index having the range filters into memorywithout reading snapshot indices into memory.